Integrated data driven analysis identifies potential candidate genes associated with PCOS.

Area of Science:

• Genomics
• Molecular Biology
• Reproductive Endocrinology

Background:

• Polycystic ovary syndrome (PCOS) is a leading cause of anovulatory infertility.
• Existing PCOS research has generated extensive data, yet etiological understanding remains incomplete.
• Integrated analysis of PCOS data can identify novel candidate genes for its pathophysiology.

Purpose of the Study:

• To integrate literature and gene expression data for PCOS.
• To apply gene prioritization and network analysis for novel candidate gene discovery.
• To assess evidence-based scoring for ranking PCOS-associated genes.

Main Methods:

• Integrated evidence-based approach combining literature and gene expression data.
• Gene prioritization and network analysis for candidate gene identification.
• In silico and experimental validation of candidate genes, including protein level analysis in follicular fluid.

Main Results:

• Identified approximately 2000 plausible candidate genes associated with PCOS.
• In silico validation confirmed 938 genes involved in PCOS.
• Experimental validation showed PROS1, C1QA, and KNG1 downregulation in PCOS patients, while VTN expression was unchanged.

Conclusions:

• In silico approaches are effective for identifying and prioritizing candidate genes in complex diseases like PCOS.
• The study provides clearer insights into PCOS molecular mechanisms by adding to the repertoire of PCOS-associated genes.
• Findings highlight the role of complement and coagulation pathways in PCOS pathogenesis.